{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from MalardClient.MalardClient import MalardClient\n",
    "from MalardClient.DataSet import DataSet\n",
    "from MalardClient.BoundingBox import BoundingBox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-05-02 10:45:33\n"
     ]
    }
   ],
   "source": [
    "file = 'ATL06_20190502104533_05220304_001_01.h5'\n",
    "\n",
    "path = \"/data/slug1/holding/ICESat/\"\n",
    "\n",
    "\n",
    "hf = h5py.File(\"{}{}\".format(path, file), 'r')\n",
    "\n",
    "matchObj = re.findall(r'ATL06_(\\d+)', file)\n",
    "dataTime = datetime.strptime(matchObj[0], '%Y%m%d%H%M%S')\n",
    "\n",
    "print(dataTime)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['METADATA', 'ancillary_data', 'gt1l', 'gt1r', 'gt2l', 'gt2r', 'gt3l', 'gt3r', 'orbit_info', 'quality_assessment']>\n"
     ]
    }
   ],
   "source": [
    "print(hf.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7efda809eef0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "tracks = [  t for t in hf.keys() if t.startswith(\"gt\")  ]\n",
    "\n",
    "\n",
    "def mask_fillvalue( lons, lats, elevs, rms, fillValue ):\n",
    "    \n",
    "    \n",
    "    mask = [ i for i,e in enumerate(elevs) if e < 100000 ]\n",
    "        \n",
    "    lonsM = []\n",
    "    latsM = []\n",
    "    elevsM = []\n",
    "    rmsM = []\n",
    "        \n",
    "    for i in mask:\n",
    "        lonsM.append(lons[i])\n",
    "        latsM.append(lats[i])\n",
    "        elevsM.append(elevs[i])\n",
    "        rmsM.append(rms[i])\n",
    "        \n",
    "    return ( lonsM, latsM, elevsM, rmsM )\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "for t in tracks:\n",
    "    for h in hf[t]['land_ice_segments']:\n",
    "        fillValue = 3.4028235E38\n",
    "        elevs = hf[t]['land_ice_segments']['h_li']\n",
    "        rms =  hf[t]['land_ice_segments']['h_li_sigma']\n",
    "        lats = hf[t]['land_ice_segments']['latitude']\n",
    "        longs = hf[t]['land_ice_segments']['longitude']\n",
    "        \n",
    "        ( lonsM, latsM, elevsM, rmsM ) = mask_fillvalue( longs, lats, elevs, rms, fillValue )\n",
    "        \n",
    "        plt.scatter(x=lonsM, y=latsM, c=rmsM, marker='.', s=0.1)\n",
    "\n",
    "plt.colorbar()\n",
    "        \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = MalardClient()\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "ds = DataSet( 'test','icesat2','greenland' )\n",
    "\n",
    "bb = client.boundingBox(ds)\n",
    "\n",
    "results = client.executeQuery(ds, bb )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5290\n"
     ]
    }
   ],
   "source": [
    "df = results.to_df\n",
    "\n",
    "print( len(df) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7efd93d9cb38>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df['lon'], y=df['lat'], c=df['elev'], marker='.', s=0.1)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['lon', 'lat', 'elev', 'heading', 'demDiff', 'demDiffMad', 'demDiffMad2',\n",
      "       'phaseAmb', 'meanDiffSpread', 'wf_number', 'sampleNb', 'powerScaled',\n",
      "       'powerdB', 'phase', 'phaseS', 'phaseSSegment', 'phaseConfidence', 'coh',\n",
      "       'x', 'y', 'time', 'swathFileId', 'rowId'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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